Automated Motion Analysis for Posture, Gait, and Tremor Diagnosis
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Solution Overview
Problem
Physical therapists lack a digital system for automated diagnosis and longitudinal assessment of musculoskeletal and neurological disorders, particularly in the context of posture analysis and tremor detection, which is crucial for effective treatment planning and monitoring.
Innovation Solution
An intelligent and automated system incorporating multiple sensors and cameras, machine learning algorithms, and 3D mapping technology to analyze patient motion, posture, and gait, along with modules for tremor detection and audiovisual diagnostics, to provide personalized treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical therapists manually examine patient pose and posture, then diagnostic accuracy can be achieved, but time consumption and labor intensity increase
Solution Approach 1:
The patent replaces manual mechanical examination by physical therapists with an automated optical measurement system using cameras and machine learning algorithms. The system captures images of patients in various positions and uses AI to automatically analyze posture, alignment, and movement patterns, eliminating the need for manual measurement while maintaining diagnostic accuracy.
Solution Approach 2:
The system enables self-assessment capability where patients can be examined through automated image capture and analysis without requiring continuous professional intervention. The machine learning model independently processes images to generate diagnostic assessments, allowing the system to serve itself in the diagnostic process.
2Loss of information
If comprehensive manual assessment of pose, posture, and movement is performed, then diagnostic completeness improves, but device complexity and operational difficulty increase
Solution Approach 1:
The patent creates a multi-functional system where a single integrated platform performs multiple diagnostic tasks including static posture analysis, dynamic movement assessment, tremor detection, and longitudinal progress tracking. The same camera system and machine learning model handle various assessment types, eliminating the need for multiple separate devices and simplifying operation.
Solution Approach 2:
The system divides the comprehensive assessment into modular functional components: image capture module, tremor detection module, posture analysis module, and progress tracking module. Each module handles specific aspects of diagnosis independently but integrates seamlessly, making the complex system manageable and easier to operate.
3Reliability
If longitudinal assessment of patient progression is implemented, then treatment effectiveness monitoring improves, but data management complexity increases
Solution Approach 1:
The system implements automated feedback loops where patient progress data captured over time is continuously analyzed and compared against treatment goals. The machine learning model generates automated progress reports and treatment recommendations, providing real-time feedback to therapists and patients without manual data management intervention.
Solution Approach 2:
The system creates digital copies of patient assessments through standardized image capture protocols and data structures. Each assessment session generates consistent digital replicas of physical examinations that can be stored, compared, and analyzed automatically, simplifying longitudinal data management through standardized digital representation.
Data Source
AI summary
A method comprising to analyze the captured video data of the patient's body parts and to generate patient motion metrics, patient posture metrics, and patient gait metrics for each exercise routine completed by the patient based on the analyzed video data; store, in a database, the generated patient motion metrics, the patient posture metrics and the patient gait metrics for each of the completed exercise routines, wherein the generated patient motion metrics, the generated patient posture metrics and the generated patient gait metrics are used to track patient progress during physical therapy sessions, diagnose movement disorders, and provide personalized, data-driven treatment plans to enhance immediate outcomes and long-term recovery with respect to the musculoskeletal and neurological disorders; apply motion amplification algorithms to enhance the additional video data to provide clarity of tremor movements; apply edge detection to enhance tremor movement boundaries and detection; generate tremor amplitude measurements and tremor frequency measurements based on the motion amplification and edge detection algorithms; and store, in the database, the generated tremor amplitude measurements and tremor frequency measurements, wherein the generated tremor amplitude measurements and tremor frequency measurements are analyzed for diagnosis and monitoring of disorders such as Parkinson's disease and Essential Tremor.